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This paper proposes a novel approach to provide directional forecasts for carry trade strategies; this approach is based on Support Vector Machines (SVM), a learning algorithm which delivers extremely promising results. Building on recent findings of the literature on carry trade we condition the SVM on indicators of uncertainty and risk; we show that this provides a dramatic improvement of the performance of the strategy, in particular during periods of financial distress such as the recentdoi:10.2139/ssrn.2728990 fatcat:m3w3nceh5bfmpmpg525coqk6sm